UC Berkeley

Identifying phase information is biomedically important due to the association of complex haplotype effects, such as compound heterozygosity, with disease. As recent next-generation sequencing (NGS) technologies provide more read sequences, the use of diverse sequencing datasets for haplotype phasing is now possible, allowing haplotype reconstruction of a single sequenced individual using NGS data. Nearly all previous haplotype reconstruction studies have focused on diploid genomes and are rarely scalable to genomes with higher ploidy. Yet computational investigations into polyploid genomes carry great importance, impacting plant, yeast and fish genomics, as well as the studies of the evolution of modern-day eukaryotes and (epi)genetic interactions between copies of genes. Furthermore, previous diploid haplotype reconstruction studies have ignored differential allele-specific expression in whole transcriptome sequencing (RNA-seq) data; however, intuition suggests that the asymmetry in this data (i.e. maternal and paternal haplotypes of a gene are differentially expressed) can be exploited to improve phasing power. In this thesis, we describe novel integrative maximum-likelihood estimation frameworks, HapTree and HapTree-X, for efficient, scalable haplotype assembly from NGS data. HapTree is built to recover an individual polyploid genome from genomic read data, and HapTree-X aims to reconstruct a diploid genome or transcriptome from RNA-seq and DNA-seq data by making use of differential allele-specific expression. HapTree-X is the first method for haplotype assembly that uses differential expression, newly allowing the use of reads that cover only one SNP.

For triploid and higher ploidy genomes, we demonstrate that HapTree substantially improves haplotype assembly accuracy and efficiency over the state-of-the-art; moreover, HapTree is the first scalable polyplotyping method for higher ploidy. As a proof of concept, we also test our method on real sequencing data from NA12878 (1000 Genomes Project) and evaluate the quality of assembled haplotypes with respect to trio-based diplotype annotation as the ground truth. The results indicate that HapTree significantly improves the switch accuracy within phased haplotype blocks as compared to existing haplotype assembly methods, while producing comparable minimum error correction (MEC) values. We evaluate the performance of HapTree-X on real sequencing read data, both transcriptomic and genomic, from NA12878 (1000 Genomes Project and Gencode) and demonstrate that HapTree-X increases the number of SNPs that can be phased and sizes of phased-haplotype blocks, without compromising accuracy. We prove theoretical bounds on the precise improvement of accuracy as a function of coverage which can be achieved from differential expression-based methods alone. Thus, the advantage of our integrative approach substantially grows as the amount of RNA-seq data increases.